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An artificial neural network-based critical values for multiple hypothesis testing: data-snooping case

2021; Taylor & Francis; Volume: 54; Issue: 386 Linguagem: Inglês

10.1080/00396265.2021.1968176

1752-2706

Vinícius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Maria Luísa Silva Bonimani, Bruno Póvoa Rodrigues, Caio César de Campos, Maurício Roberto Veronez, Luiz Gonzaga,

Advanced Statistical Methods and Models

Data Snooping is the most best-established method for identifying outliers in geodetic data analysis. It has been demonstrated in the literature that to effectively user-control the type I error rate, critical values must be computed numerically by means of Monte Carlo. Here, on the other hand, we provide a model based on an artificial neural network. The results prove that the proposed model can be used to compute the critical values and, therefore, it is no longer necessary to run the Monte Carlo-based critical value every time the quality control is performed by means of data snooping.